Stock Technical Indicators
Data Unicorns
December 8, 2017
Introduction
Stock Technical Analysis is the analysis of the term used for the Technical Analysis of the stocks, which is the analysis and study of the pattern of changes in the stock price of a particular stock. Technical Analysis has been employed by some of the professional traders to know the trends and get signals from the stocks as to when to sell them or buy a specific stock. This can maximise the profit, although it is not going to predict the stock pattern accurately or provide much feedback about the future, it can be helpful in the analysis of the stocks to prepare for the future trends and investments.
There a huge range of charts and analysis in the stock technical analysis, which will be dealt in depth in the following exposition in R.
Retrieving the Data
In order to start the analysis, it is required to get the data from some of the reliable sources and load it into R. The packages like quantmod will be helpful in getting the financial data for some of the companies under consideration.
# Load R packages
library("TTR") #Technical Trading Rules Package
library("quantmod") # Quantitative Financial Modelling and Trading Framework for R
library("PerformanceAnalytics") #Econometric tools for performance and risk analysis
#Get data from Google for Apple stock
getSymbols("AAPL",src="google")## [1] "AAPL"The data is now loaded in a dataframe under the name AAPL. In order to start the analysis, we need to slice and dice the dataset to the required timeframe that is under the consideration.
In order to keep the trend of the current pattern and also have the minimum required data points at hand, 5 years worth of the stock data is preffered.
#Delimit data range for past 5 years from Dec-2017
aapl <- window(AAPL['2012-01-01::2017-12-08'])
# Technical Analysis Charts
lineChart(aapl) # Line Charts
barChart(aapl) # Bar Charts
candleChart(aapl, name = "Candle Chart for AAPL") # Candle Charts
Candle Charts are used to unserstand the variation of the stock on a daily basis. The candle stick chart is produced on a daily basis for a stock. The following image clearly explains the various aspects of a Candle Stick Chart.
Candle Stick Chart Explanation
candleChart(tail(AAPL), name = "Candle Chart for Past few Days") # Candle Chart for last few days
Stock Technical Indicators
Stock Technical Indicators are used in the arena of Technical Analysis to estimate the variation in the supply and demand of stocks. Various Indicators are developed for a stock to analyse the pattern of Stock variation on a daily basis. Technical Indicators, collectively called as the “Technicals”, are different from other simple indicators as they do not analyse any single part of the fundamental business like earnings, revenue or profit margins, which show the direct performance of the business. The technicals are used by active traders to assess the changes in the stock and develop the investment plans and protfolio management. However, most of the technicals are of little use to the long term investor due to fact that the trend is analysed at a subtle level and this pattern is short lived as the industry learns the pattern sooner or later. As the industry learns the pattern, the pattern tends to change, resulting in the change in the technical indicator values. Thus, the ever changing technicals cannot be an able guide to the a long term investor, whereas they are the guiding indicators for the active short term investors.
There are two kinds of main Technicals, i.e, Lagging & Leading. Indicators are the economic factors that change either before or after the start of the economy. The indicators that change before the economy has begun are called Leading indicators, as they even lead before the start of the economy. The indicators that change after the economy has started are called as Lagging Indicators, as they change after the start of the economy, they are lagging to show the change.
Leading indicators are generally used to predict changes in the economy, however, they are not always accurate. The lagging indicators trail the changes in the price action of the stock, which generate transaction signals and also confirm the strength of the leading indicator trend. Thus, the lagging indicators are more relied upon compared to the leading indicators.
Let us now take a look at some of the Technical Indicators in R. Further reading related to the stocks can be found in the references.
Lagging Technical Indicators
# 2.1. Lagging Stock Technical Indicators
# 2.1.1. Moving Averages MA, Simple Moving Averages SMA(5 & 21), Exponential Moving Averages EMA(5 & 21)
# Simple Moving Average
sma5 <- SMA(Cl(aapl),n=5)
sma21 <- SMA(Cl(aapl),n=21)
# Technical Analysis Chart
barChart(aapl)
addSMA(n=5,col=4)
addSMA(n=21,col=6)
# Manual Chart
plot(Cl(aapl),main="Simple Moving Averages SMA(5 & 21)")
lines(sma5,col=4)
lines(sma21,col=6)
# Exponential Moving Average
ema5 <- EMA(Cl(aapl),n=5)
ema21 <- EMA(Cl(aapl),n=21)
# Technical Analysis Chart
barChart(aapl)
addEMA(n=5,col=4)
addEMA(n=21,col=6)
# Manual Chart
plot(Cl(aapl),main="Exponential Moving Averages EMA(5 & 21)")
lines(ema5,col=4)
lines(ema21,col=6)
# 2.1.2. Bollinger Bands BB(20,2)
bb <- BBands(cbind(Hi(aapl),Lo(aapl),Cl(aapl)),n=20,sd=2)
# Technical Analysis Chart
barChart(aapl)
addBBands(n=20,sd=2)
# Manual Chart
plot(Cl(aapl),main="Bollinger Bands BB(20,2)")
# Lower and Upper Bands
lines(bb[,1],col=4)
lines(bb[,3],col=4)
# Middle Band
lines(bb[,2],col=5)
# 2.1.3. Parabolic Stop and Reverse SAR(0.02,0.2)
sar <- SAR(cbind(Hi(aapl),Lo(aapl)),accel=c(0.02, 0.2))
# Technical Analysis Chart
barChart(aapl)
addSAR(accel=c(0.02, 0.2))
# Manual Chart
plot(Cl(aapl),main="Parabolic Stop and Reverse SAR(0.02,0.2)")
points(sar,col=4)
Leading Indicators
# 2.2. Leading Stock Technical Indicators
# 2.2.1. Average Directional Movement Index ADX(14)
adx <- ADX(cbind(Hi(aapl),Lo(aapl),Cl(aapl)),n=14)
# Technical Analysis Chart
barChart(aapl)
addADX(n=14)
# 2.2.2. Commodity Channel Index CCI(20,0.015)
cci <- CCI(cbind(Hi(aapl),Lo(aapl),Cl(aapl)),n=20,c=0.015)
# Technical Analysis Chart
barChart(aapl)
addCCI(n=20,c=0.015)
# 2.2.3. Moving Averages Covergence/Divergence MACD(12,26,9)
macd <- MACD(Cl(aapl),nFast=12,nSlow=26,nSig=9)
# Technical Analysis Chart
barChart(aapl)
addMACD()
# 2.2.4. Rate Of Change ROC(21)
roc <- ROC(aapl,n=21)
# Technical Analysis Chart
barChart(aapl)
addROC(n=21)
# 2.2.5. Relative Strength Index RSI(14)
rsi <- RSI(Cl(aapl),n=14)
# Technical Analysis Chart
barChart(aapl)
addRSI(n=14)
# 2.2.6. Stochastic Momentum Index SMI(13,2,25,9)
smi <- SMI(cbind(Hi(aapl),Lo(aapl),Cl(aapl)),n=13,nFast=2,nSlow=25,nSig=9)
# Technical Analysis Chart
barChart(aapl)
addSMI(n=13)
# 2.2.7. Williams %R(14)
wpr <- WPR(cbind(Hi(aapl),Lo(aapl),Cl(aapl)),n=14)
# Technical Analysis Chart
barChart(aapl)
addWPR(n=14)
Stock Technical Signals
# 3. Stock Trading Signals
# 3.1. Single Indicator Trading Signals
# 3.1.1. Moving Averages MA, Simple Moving Averages SMA(5 & 21), Exponential Moving Averages EMA(5 & 21) Trading Signals
# Simple Moving Averages SMA(5 & 21) Price Crossover Trading Signals
lineChart(aapl)
addSMA(n=5,col=4)
addSMA(n=21,col=6)
# Price Crossover Trading Signals
sma5tr <- Lag(ifelse(Lag(Cl(aapl))<Lag(sma5)&Cl(aapl)>sma5,1,ifelse(Lag(Cl(aapl))>Lag(sma5)&Cl(aapl)<sma5,-1,0)))
sma5tr[is.na(sma5tr)] <- 0
sma21tr <- Lag(ifelse(Lag(Cl(aapl))<Lag(sma21)&Cl(aapl)>sma21,1,ifelse(Lag(Cl(aapl))>Lag(sma21)&Cl(aapl)<sma21,-1,0)))
sma21tr[is.na(sma21tr)] <- 0
# Exponential Moving Averages EMA (5 & 21) Price Crossover Trading Signals
lineChart(aapl)
addEMA(n=5,col=4)
addEMA(n=21,col=6)
# Price Crossover Trading Signals
ema5tr <- Lag(ifelse(Lag(Cl(aapl))<Lag(ema5)&Cl(aapl)>ema5,1,ifelse(Lag(Cl(aapl))>Lag(ema5)&Cl(aapl)<ema5,-1,0)))
ema5tr[is.na(ema5tr)] <- 0
ema21tr <- Lag(ifelse(Lag(Cl(aapl))<Lag(ema21)&Cl(aapl)>ema21,1,ifelse(Lag(Cl(aapl))>Lag(ema21)&Cl(aapl)<ema21,-1,0)))
ema21tr[is.na(ema21tr)] <- 0
# Simple & Exponential Moving Averages SMA(5 & 21) & EMA(5 & 21) Double Crossover Trading Signals
# Double Crossover Trading Signals
smatr <- Lag(ifelse(Lag(sma5)<Lag(sma21)&sma5>sma21,1,ifelse(Lag(sma5)>Lag(sma21)&sma5<sma21,-1,0)))
smatr[is.na(smatr)] <- 0
ematr <- Lag(ifelse(Lag(ema5)<Lag(ema21)&ema5>ema21,1,ifelse(Lag(ema5)>Lag(ema21)&ema5<ema21,-1,0)))
ematr[is.na(ematr)] <- 0
# 3.1.2. Bollinger Bands BB(20,2) Trading Signals
lineChart(aapl)
addBBands(n=20,sd=2)
# Bands Crossover Trading Signals
bbtr <- Lag(ifelse(Lag(Cl(aapl))<Lag(bb[,1])&Cl(aapl)>bb[,1],1,ifelse(Lag(Cl(aapl))<Lag(bb[,3])&Cl(aapl)>bb[,3],-1,0)))
bbtr[is.na(bbtr)] <- 0
# 3.1.3. Parabolic Stop And Reverse SAR(0.02,0.2) Trading Signals
lineChart(aapl)
addSAR(accel=c(0.02, 0.2))
# Stop And Reverse Trading Signals
sartr <- Lag(ifelse(Lag(Cl(aapl))<Lag(sar)&Cl(aapl)>sar,1,ifelse(Lag(Cl(aapl))>Lag(sar)&Cl(aapl)<sar,-1,0)))
sartr[is.na(sartr)] <- 0
# 3.1.4. Average Directional Movement Index ADX(14) Trading Signals
lineChart(aapl)
addADX(n=14)
# Band and Double Crossover Trading Signals
adxtr <- Lag(ifelse(Lag(adx[,1])<Lag(adx[,2])&adx[,1]>adx[,2]&adx[,4]>20,1,ifelse(Lag(adx[,1])>Lag(adx[,2])&adx[,1]<adx[,2]&adx[,4]>20,-1,0)))
adxtr[is.na(adxtr)] <- 0
# 3.1.5. Commodity Channel Index CCI(20,0.015) Trading Signals
lineChart(aapl)
addCCI(n=20,c=0.015)
# Bands Crossover Trading Signals
ccitr <- Lag(ifelse(Lag(cci)<(-100)&cci>(-100),1,ifelse(Lag(cci)<100&cci>100,-1,0)))
ccitr[is.na(ccitr)] <- 0
# 3.1.6. Moving Averages Covergence/Divergence MACD(12,26,9) Trading Signals
lineChart(aapl)
addMACD()
# Signal and Centerline Crossover Trading Signals
smacdtr <- Lag(ifelse(Lag(macd[,1])<Lag(macd[,2])&macd[,1]>macd[,2],1,ifelse(Lag(macd[,1])>Lag(macd[,2])&macd[,1]<macd[,2],-1,0)))
smacdtr[is.na(smacdtr)] <- 0
cmacdtr <- Lag(ifelse(Lag(macd[,1])<0&macd[,1]>0,1,ifelse(Lag(macd[,1])>0&macd[,1]<0,-1,0)))
cmacdtr[is.na(cmacdtr)] <- 0
# 3.1.7. Rate Of Change ROC(21) Trading Signals
lineChart(aapl)
addROC(n=21)
# Bands Crossover Trading Signals
roctr <- Lag(ifelse(Lag(roc[,4])<(-0.10)&roc[,4]>(-0.10),1,ifelse(Lag(roc[,4])<0.10&roc[,4]>0.10,-1,0)))
roctr[is.na(roctr)] <- 0
# 3.1.8. Relative Strength Index RSI(14) Trading Signals
lineChart(aapl)
addRSI(n=14)
# Bands Crossover Trading Signals
rsitr <- Lag(ifelse(Lag(rsi)<30&rsi>30,1,ifelse(Lag(rsi)<70&rsi>70,-1,0)))
rsitr[is.na(rsitr)] <- 0
# 3.1.9. Stochastic Momentum Index SMI(13,2,25,9) Trading Signals
lineChart(aapl)
addSMI(n=13)
# Signal Crossover Trading Signals
smitr <- Lag(ifelse(Lag(smi[,1])<Lag(smi[,2])&smi[,1]>smi[,2],1,ifelse(Lag(smi[,1])>Lag(smi[,2])&smi[,1]<smi[,2],-1,0)))
smitr[is.na(smitr)] <- 0
# 3.1.10. Williams %R(14) Trading Signals
lineChart(aapl)
addWPR(n=14)
# Bands Crossover Trading Signals
wprtr <- Lag(ifelse(Lag(wpr)>0.80&wpr<0.80,1,ifelse(Lag(wpr)>0.20&wpr<0.20,-1,0)))
wprtr[is.na(wprtr)] <- 0
# 3.2. Multiple Indicators Trading Signals
# 3.2.1. Commodity Channel Index CCI(20,0.015) and Simple Moving Average SMA(5) Trading Signals
lineChart(aapl)
addCCI(n=20,c=0.015)
addSMA(n=5,col=4)
# Price Crossover and Bands Crossover Confirmation Trading Signals
ccismatr <- Lag(ifelse(Lag(Cl(aapl))<Lag(sma5)&Cl(aapl)>sma5&cci<(-100),1,ifelse(Lag(Cl(aapl))>Lag(sma5)&Cl(aapl)<sma5&cci>100,-1,0)))
ccismatr[is.na(ccismatr)] <- 0
# 3.2.2. Rate Of Change ROC(21) and Simple Moving Average SMA(5) Trading Signals
lineChart(aapl)
addROC(n=21)
addSMA(n=5,col=4)
# Price Crossover and Bands Crossover Confirmation Trading Signals
rocsmatr <- Lag(ifelse(Lag(Cl(aapl))<Lag(sma5)&Cl(aapl)>sma5&roc[,4]<(-0.10),1,ifelse(Lag(Cl(aapl))>Lag(sma5)&Cl(aapl)<sma5&roc[,4]>0.10,-1,0)))
rocsmatr[is.na(rocsmatr)] <- 0
# 3.2.3. Relative Strength Index RSI(14) and Simple Moving Average SMA(5) Trading Signals
lineChart(aapl)
addRSI(n=14)
addSMA(n=5,col=4)
# Price Crossover and Bands Crossover Confirmation Trading Signals
rsismatr <- Lag(ifelse(Lag(Cl(aapl))<Lag(sma5)&Cl(aapl)>sma5&rsi<30,1,ifelse(Lag(Cl(aapl))>Lag(sma5)&Cl(aapl)<sma5&rsi>70,-1,0)))
rsismatr[is.na(rsismatr)] <- 0
# 3.2.4. Stochastic Momentum Index SMI(13,2,25,9) and Simple Moving Average SMA(5) Trading Signals
lineChart(aapl)
addSMI(n=13)
addSMA(n=5,col=4)
# Price Crossover and Bands Crossover Confirmation Trading Signals
smismatr <- Lag(ifelse(Lag(Cl(aapl))<Lag(sma5)&Cl(aapl)>sma5&smi[,1]<(-40),1,ifelse(Lag(Cl(aapl))>Lag(sma5)&Cl(aapl)<sma5&smi[,1]>40,-1,0)))
smismatr[is.na(smismatr)] <- 0
# 3.2.5. Williams %R(14) and Simple Moving Average SMA(5) Trading Signals
lineChart(aapl)
addWPR(n=14)
addSMA(n=5,col=4)
# Price Crossover and Bands Crossover Confirmation Trading Signals
wprsmatr <- Lag(ifelse(Lag(Cl(aapl))<Lag(sma5)&Cl(aapl)>sma5&wpr>0.80,1,ifelse(Lag(Cl(aapl))>Lag(sma5)&Cl(aapl)<sma5&wpr<0.20,-1,0)))
wprsmatr[is.na(wprsmatr)] <- 0Stock Trading Strategies
# 4. Stock Trading Strategies
# 4.1. Moving Averages MA, Simple Moving Averages SMA(5 & 21), Exponential Moving Averages EMA(5 & 21) Trading Strategies
# Simple Moving Averages SMA(5 & 21) Price Crossover Trading Strategies
# Price Crossover Trading Strategies
sma5sig <- ifelse(sma5tr>1,0,1)
for(i in 1:length(Cl(aapl))){sma5sig[i] <- ifelse(sma5tr[i]==1,1,ifelse(sma5tr[i]==-1,0,sma5sig[i-1]))}
sma5sig[is.na(sma5sig)] <- 1
sma21sig <- ifelse(sma21tr>1,0,1)
for(i in 1:length(Cl(aapl))){sma21sig[i] <- ifelse(sma21tr[i]==1,1,ifelse(sma21tr[i]==-1,0,sma21sig[i-1]))}
sma21sig[is.na(sma21sig)] <- 1
# Exponential Moving Averages EMA (5 & 21) Price Crossover Trading Strategies
# Price Crossover Trading Strategies
ema5sig <- ifelse(ema5tr>1,0,1)
for(i in 1:length(Cl(aapl))){ema5sig[i] <- ifelse(ema5tr[i]==1,1,ifelse(ema5tr[i]==-1,0,ema5sig[i-1]))}
ema5sig[is.na(ema5sig)] <- 1
ema21sig <- ifelse(ema21tr>1,0,1)
for(i in 1:length(Cl(aapl))){ema21sig[i] <- ifelse(ema21tr[i]==1,1,ifelse(ema21tr[i]==-1,0,ema21sig[i-1]))}
ema21sig[is.na(ema21sig)] <- 1
# Simple & Exponential Moving Averages SMA(5 & 21) & EMA(5 & 21) Double Crossover Trading Strategies
# Double Crossover Trading Strategies
smasig <- ifelse(smatr>1,0,1)
for(i in 1:length(Cl(aapl))){smasig[i] <- ifelse(smatr[i]==1,1,ifelse(smatr[i]==-1,0,smasig[i-1]))}
smasig[is.na(smasig)] <- 1
emasig <- ifelse(ematr>1,0,1)
for(i in 1:length(Cl(aapl))){emasig[i] <- ifelse(ematr[i]==1,1,ifelse(ematr[i]==-1,0,emasig[i-1]))}
emasig[is.na(emasig)] <- 1
# 4.2. Bollinger Bands BB(20,2) Trading Strategy
# Bands Crossover Trading Strategy
bbsig <- ifelse(bbtr>1,0,1)
for(i in 1:length(Cl(aapl))){bbsig[i] <- ifelse(bbtr[i]==1,1,ifelse(bbtr[i]==-1,0,bbsig[i-1]))}
bbsig[is.na(bbsig)] <- 1
# 4.3. Parabolic Stop And Reverse SAR(0.02,0.2) Trading Strategy
# Stop And Reverse Trading Strategy
sarsig <- ifelse(sartr>1,0,1)
for(i in 1:length(Cl(aapl))){sarsig[i] <- ifelse(sartr[i]==1,1,ifelse(sartr[i]==-1,0,sarsig[i-1]))}
sarsig[is.na(sarsig)] <- 1
# 4.4. Average Directional Movement Index ADX(14) Trading Strategy
# Band and Double Crossover Trading Strategy
adxsig <- ifelse(adxtr>1,0,1)
for(i in 1:length(Cl(aapl))){adxsig[i] <- ifelse(adxtr[i]==1,1,ifelse(adxtr[i]==-1,0,adxsig[i-1]))}
adxsig[is.na(adxsig)] <- 1
# 4.5. Commodity Channel Index CCI(20,0.015) Trading Strategy
# Bands Crossover Trading Strategy
ccisig <- ifelse(ccitr>1,0,1)
for(i in 1:length(Cl(aapl))){ccisig[i] <- ifelse(ccitr[i]==1,1,ifelse(ccitr[i]==-1,0,ccisig[i-1]))}
ccisig[is.na(ccisig)] <- 1
# 4.6. Moving Averages Covergence/Divergence MACD(12,26,9) Trading Strategies
# Signal and Centerline Crossover Trading Strategies
smacdsig <- ifelse(smacdtr>1,0,1)
for(i in 1:length(Cl(aapl))){smacdsig[i] <- ifelse(smacdtr[i]==1,1,ifelse(smacdtr[i]==-1,0,smacdsig[i-1]))}
smacdsig[is.na(smacdsig)] <- 1
cmacdsig <- ifelse(cmacdtr>1,0,1)
for(i in 1:length(Cl(aapl))){cmacdsig[i] <- ifelse(cmacdtr[i]==1,1,ifelse(cmacdtr[i]==-1,0,cmacdsig[i-1]))}
cmacdsig[is.na(cmacdsig)] <- 1
# 4.7. Rate Of Change ROC(21) Trading Strategy
# Bands Crossover Trading Strategy
rocsig <- ifelse(roctr>1,0,1)
for(i in 1:length(Cl(aapl))){rocsig[i] <- ifelse(roctr[i]==1,1,ifelse(roctr[i]==-1,0,rocsig[i-1]))}
rocsig[is.na(rocsig)] <- 1
# 4.8. Relative Strength Index RSI(14) Trading Strategy
# Bands Crossover Trading Strategy
rsisig <- ifelse(rsitr>1,0,1)
for(i in 1:length(Cl(aapl))){rsisig[i] <- ifelse(rsitr[i]==1,1,ifelse(rsitr[i]==-1,0,rsisig[i-1]))}
rsisig[is.na(rsisig)] <- 1
# 4.9. Stochastic Momentum Index SMI(13,2,25,9) Trading Strategy
# Signal Crossover Trading Strategy
smisig <- ifelse(smitr>1,0,1)
for(i in 1:length(Cl(aapl))){smisig[i] <- ifelse(smitr[i]==1,1,ifelse(smitr[i]==-1,0,smisig[i-1]))}
smisig[is.na(smisig)] <- 1
# 4.10. Williams %R(14) Trading Strategy
# Bands Crossover Trading Strategy
wprsig <- ifelse(wprtr>1,0,1)
for(i in 1:length(Cl(aapl))){wprsig[i] <- ifelse(wprtr[i]==1,1,ifelse(wprtr[i]==-1,0,wprsig[i-1]))}
wprsig[is.na(wprsig)] <- 1
# 4.11. Commodity Channel Index CCI(20,0.015) and Simple Moving Average SMA(5) Trading Strategy
# Price Crossover and Bands Crossover Confirmation Trading Strategy
ccismasig <- ifelse(ccismatr>1,0,1)
for(i in 1:length(Cl(aapl))){ccismasig[i] <- ifelse(ccismatr[i]==1,1,ifelse(ccismatr[i]==-1,0,ccismasig[i-1]))}
ccismasig[is.na(ccismasig)] <- 1
# 4.12. Rate Of Change ROC(21) and Simple Moving Average SMA(5) Trading Strategy
# Price Crossover and Bands Crossover Confirmation Trading Strategy
rocsmasig <- ifelse(rocsmatr>1,0,1)
for(i in 1:length(Cl(aapl))){rocsmasig[i] <- ifelse(rocsmatr[i]==1,1,ifelse(rocsmatr[i]==-1,0,rocsmasig[i-1]))}
rocsmasig[is.na(rocsmasig)] <- 1
# 4.13. Relative Strength Index RSI(14) and Simple Moving Average SMA(5) Trading Strategy
# Price Crossover and Bands Crossover Confirmation Trading Strategy
rsismasig <- ifelse(rsismatr>1,0,1)
for(i in 1:length(Cl(aapl))){rsismasig[i] <- ifelse(rsismatr[i]==1,1,ifelse(rsismatr[i]==-1,0,rsismasig[i-1]))}
rsismasig[is.na(rsismasig)] <- 1
# 4.14. Stochastic Momentum Index SMI(13,2,25,9) and Simple Moving Average SMA(5) Trading Strategy
# Price Crossover and Bands Crossover Confirmation Trading Strategy
smismasig <- ifelse(smismatr>1,0,1)
for(i in 1:length(Cl(aapl))){smismasig[i] <- ifelse(smismatr[i]==1,1,ifelse(smismatr[i]==-1,0,smismasig[i-1]))}
smismasig[is.na(smismasig)] <- 1
# 4.15. Williams %R(14) and Simple Moving Average SMA(5) Trading Strategy
# Price Crossover and Bands Crossover Confirmation Trading Strategy
wprsmasig <- ifelse(wprsmatr>1,0,1)
for(i in 1:length(Cl(aapl))){wprsmasig[i] <- ifelse(wprsmatr[i]==1,1,ifelse(wprsmatr[i]==-1,0,wprsmasig[i-1]))}
wprsmasig[is.na(wprsmasig)] <- 1
##########Strategies Performance Comparison
# 5. Strategies Performance Comparison
# 5.1. Moving Averages MA, Simple Moving Averages SMA(5 & 21), Exponential Moving Averages EMA(5 & 21) Strategies Performance Comparison
# Simple Moving Averages SMA(5 & 21) Price Crossover Strategies Performance Comparison
# Price Crossover Strategy Returns/Equity Curve
ret <- ROC(Cl(aapl))
ret[1] <- 0
bhstrat <- ret
sma5strat <- ret*sma5sig
sma5stratc <- ifelse((sma5tr==1|sma5tr==-1)&sma5sig!=Lag(sma5sig),(ret-0.01)*sma5sig,ret*sma5sig)
sma21strat <- ret*sma21sig
sma21stratc <- ifelse((sma21tr==1|sma21tr==-1)&sma21sig!=Lag(sma21sig),(ret-0.01)*sma21sig,ret*sma21sig)
# Price Crossover Strategy Performance Comparison
smacomp <- cbind(sma5strat,sma5stratc,sma21strat,sma21stratc,bhstrat)
colnames(smacomp) <- c("SMA(5)","SMA(5) TC","SMA(21)","SMA(21) TC","Buy & Hold")
table.AnnualizedReturns(smacomp)## SMA(5) SMA(5) TC SMA(21) SMA(21) TC Buy & Hold
## Annualized Return 0.1053 -0.1837 0.1408 0.0011 0.1584
## Annualized Std Dev 0.1625 0.1701 0.1616 0.1664 0.2500
## Annualized Sharpe (Rf=0%) 0.6480 -1.0801 0.8710 0.0069 0.6338charts.PerformanceSummary(smacomp)
# Exponential Moving Averages EMA (5 & 21) Price Crossover Strategies Performance Comparison
# Price Crossover Strategy Returns/Equity Curve
ema5strat <- ret*ema5sig
ema5stratc <- ifelse((ema5tr==1|ema5tr==-1)&ema5sig!=Lag(ema5sig),(ret-0.01)*ema5sig,ret*ema5sig)
ema21strat <- ret*ema21sig
ema21stratc <- ifelse((ema21tr==1|ema21tr==-1)&ema21sig!=Lag(ema21sig),(ret-0.01)*ema21sig,ret*ema21sig)
# Price Crossover Strategy Performance Comparison
emacomp <- cbind(ema5strat,ema5stratc,ema21strat,ema21stratc,bhstrat)
colnames(emacomp) <- c("EMA(5)","EMA(5) TC","EMA(21)","EMA(21) TC","Buy & Hold")
table.AnnualizedReturns(emacomp)## EMA(5) EMA(5) TC EMA(21) EMA(21) TC Buy & Hold
## Annualized Return 0.1291 -0.1504 0.1282 -0.0230 0.1584
## Annualized Std Dev 0.1683 0.1753 0.1622 0.1656 0.2500
## Annualized Sharpe (Rf=0%) 0.7672 -0.8582 0.7902 -0.1387 0.6338charts.PerformanceSummary(emacomp)
# Simple & Exponential Moving Averages SMA(5 & 21) & EMA(5 & 21) Double Crossover Strategies Performance Comparison
# Double Crossover Strategy Returns/Equity Curve
smastrat <- ret*smasig
smastratc <- ifelse((smatr==1|smatr==-1)&smasig!=Lag(smasig),(ret-0.01)*smasig,ret*smasig)
emastrat <- ret*emasig
emastratc <- ifelse((ematr==1|ematr==-1)&emasig!=Lag(emasig),(ret-0.01)*emasig,ret*emasig)
# Double Crossover Strategy Performance Comparison
macomp <- cbind(smastrat,smastratc,emastrat,emastratc,bhstrat)
colnames(macomp) <- c("SMA(5 & 21)","SMA(5 & 21) TC","EMA(5 & 21)","EMA(5 & 21) TC","Buy & Hold")
table.AnnualizedReturns(macomp)## SMA(5 & 21) SMA(5 & 21) TC EMA(5 & 21)
## Annualized Return 0.1552 0.0739 0.1125
## Annualized Std Dev 0.1693 0.1725 0.1707
## Annualized Sharpe (Rf=0%) 0.9169 0.4281 0.6589
## EMA(5 & 21) TC Buy & Hold
## Annualized Return 0.0326 0.1584
## Annualized Std Dev 0.1730 0.2500
## Annualized Sharpe (Rf=0%) 0.1882 0.6338charts.PerformanceSummary(macomp)
# 5.2. Bollinger Bands BB(20,2) Strategy Performance Comparison
# Bands Strategy Returns/Equity Curve
bbstrat <- ret*bbsig
bbstratc <- ifelse((bbtr==1|bbtr==-1)&bbsig!=Lag(bbsig),(ret-0.01)*bbsig,ret*bbsig)
# Bands Strategy Performance Comparison
bbcomp <- cbind(bbstrat,bbstratc,bhstrat)
colnames(bbcomp) <- c("BB(20,2)","BB(20,2) TC","Buy & Hold")
table.AnnualizedReturns(bbcomp)## BB(20,2) BB(20,2) TC Buy & Hold
## Annualized Return 0.0580 0.0245 0.1584
## Annualized Std Dev 0.1863 0.1867 0.2500
## Annualized Sharpe (Rf=0%) 0.3112 0.1315 0.6338charts.PerformanceSummary(bbcomp)
# 5.3. Parabolic Stop And Reverse SAR(0.02,0.2) Strategy Performance Comparison
# Stop And Reverse Strategy Returns/Equity Curve
sarstrat <- ret*sarsig
sarstratc <- ifelse((sartr==1|sartr==-1)&sarsig!=Lag(sarsig),(ret-0.01)*sarsig,ret*sarsig)
# Stop And Reverse Strategy Performance Comparison
sarcomp <- cbind(sarstrat,sarstratc,bhstrat)
colnames(sarcomp) <- c("SAR(0.02,0.2)","SAR(0.02,0.2) TC","Buy & Hold")
table.AnnualizedReturns(sarcomp)## SAR(0.02,0.2) SAR(0.02,0.2) TC Buy & Hold
## Annualized Return 0.0727 -0.0343 0.1584
## Annualized Std Dev 0.1690 0.1725 0.2500
## Annualized Sharpe (Rf=0%) 0.4299 -0.1992 0.6338charts.PerformanceSummary(sarcomp)
# 5.4. Average Directional Movement Index ADX(14) Strategy Performance Comparison
# Band and Double Crossover Strategy Returns/Equity Curve
adxstrat <- ret*adxsig
adxstratc <- ifelse((adxtr==1|adxtr==-1)&adxsig!=Lag(adxsig),(ret-0.01)*adxsig,ret*adxsig)
# Band and Double Crossover Strategy Performance Comparison
adxcomp <- cbind(adxstrat,adxstratc,bhstrat)
colnames(adxcomp) <- c("ADX(14)","ADX(14) TC","Buy & Hold")
table.AnnualizedReturns(adxcomp)## ADX(14) ADX(14) TC Buy & Hold
## Annualized Return 0.1461 0.1082 0.1584
## Annualized Std Dev 0.1638 0.1635 0.2500
## Annualized Sharpe (Rf=0%) 0.8921 0.6618 0.6338charts.PerformanceSummary(adxcomp)
# 5.5. Commodity Channel Index CCI(20,0.015) Strategy Performance Comparison
# Bands Crossover Strategy Returns/Equity Curve
ccistrat <- ret*ccisig
ccistratc <- ifelse((ccitr==1|ccitr==-1)&ccisig!=Lag(ccisig),(ret-0.01)*ccisig,ret*ccisig)
# Bands Crossover Strategy Performance Comparison
ccicomp <- cbind(ccistrat,ccistratc,bhstrat)
colnames(ccicomp) <- c("CCI(20,0.015)","CCI(20,0.015) TC","Buy & Hold")
table.AnnualizedReturns(ccicomp)## CCI(20,0.015) CCI(20,0.015) TC Buy & Hold
## Annualized Return 0.0074 -0.0343 0.1584
## Annualized Std Dev 0.1861 0.1866 0.2500
## Annualized Sharpe (Rf=0%) 0.0399 -0.1835 0.6338charts.PerformanceSummary(ccicomp)
# 5.6. Moving Averages Covergence/Divergence MACD(12,26,9) Strategies Performance Comparison
# Signal and Centerline Strategy Returns/Equity Curve
smacdstrat <- ret*smacdsig
smacdstratc <- ifelse((smacdtr==1|smacdtr==-1)&smacdsig!=Lag(smacdsig),(ret-0.01)*smacdsig,ret*smacdsig)
cmacdstrat <- ret*cmacdsig
cmacdstratc <- ifelse((cmacdtr==1|cmacdtr==-1)&cmacdsig!=Lag(cmacdsig),(ret-0.01)*cmacdsig,ret*cmacdsig)
# Signal and Centerline Strategy Performance Comparison
macdcomp <- cbind(smacdstrat,smacdstratc,cmacdstrat,cmacdstratc,bhstrat)
colnames(macdcomp) <- c("MACD(12,26) vs Signal","MACD(12,26) vs Signal TC","MACD(12,26) vs Centerline","MACD(12,26) vs Centerline TC","Buy & Hold")
table.AnnualizedReturns(macdcomp)## MACD(12,26) vs Signal MACD(12,26) vs Signal TC
## Annualized Return 0.1276 0.0394
## Annualized Std Dev 0.1682 0.1712
## Annualized Sharpe (Rf=0%) 0.7588 0.2304
## MACD(12,26) vs Centerline
## Annualized Return 0.1283
## Annualized Std Dev 0.1782
## Annualized Sharpe (Rf=0%) 0.7202
## MACD(12,26) vs Centerline TC Buy & Hold
## Annualized Return 0.0815 0.1584
## Annualized Std Dev 0.1794 0.2500
## Annualized Sharpe (Rf=0%) 0.4545 0.6338charts.PerformanceSummary(macdcomp)
# 5.7. Rate Of Change ROC(21) Strategy Performance Comparison
# Bands Crossover Strategy Returns/Equity Curve
rocstrat <- ret*rocsig
rocstratc <- ifelse((roctr==1|roctr==-1)&rocsig!=Lag(rocsig),(ret-0.01)*rocsig,ret*rocsig)
# Bands Crossover Strategy Performance Comparison
roccomp <- cbind(rocstrat,rocstratc,bhstrat)
colnames(roccomp) <- c("ROC(21)","ROC(21) TC","Buy & Hold")
table.AnnualizedReturns(roccomp)## ROC(21) ROC(21) TC Buy & Hold
## Annualized Return 0.0939 0.0775 0.1584
## Annualized Std Dev 0.1711 0.1710 0.2500
## Annualized Sharpe (Rf=0%) 0.5488 0.4530 0.6338charts.PerformanceSummary(roccomp)
# 5.8. Relative Strength Index RSI(14) Strategy Performance Comparison
# Bands Crossover Strategy Returns/Equity Curve
rsistrat <- ret*rsisig
rsistratc <- ifelse((rsitr==1|rsitr==-1)&rsisig!=Lag(rsisig),(ret-0.01)*rsisig,ret*rsisig)
# Bands Crossover Strategy Performance Comparison
rsicomp <- cbind(rsistrat,rsistratc,bhstrat)
colnames(rsicomp) <- c("RSI(14)","RSI(14) TC","Buy & Hold")
table.AnnualizedReturns(rsicomp)## RSI(14) RSI(14) TC Buy & Hold
## Annualized Return -0.0302 -0.0367 0.1584
## Annualized Std Dev 0.1663 0.1663 0.2500
## Annualized Sharpe (Rf=0%) -0.1817 -0.2209 0.6338charts.PerformanceSummary(rsicomp)
# 5.9. Stochastic Momentum Index SMI(13,2,25,9) Strategy Performance Comparison
# Signal Strategy Returns/Equity Curve
smistrat <- ret*smisig
smistratc <- ifelse((smitr==1|smitr==-1)&smisig!=Lag(smisig),(ret-0.01)*smisig,ret*smisig)
# Signal Strategy Performance Comparison
smicomp <- cbind(smistrat,smistratc,bhstrat)
colnames(smicomp) <- c("SMI(13,2,25) vs Signal","SMI(13,2,25) vs Signal TC","Buy & Hold")
table.AnnualizedReturns(smicomp)## SMI(13,2,25) vs Signal SMI(13,2,25) vs Signal TC
## Annualized Return 0.1480 0.0569
## Annualized Std Dev 0.1690 0.1698
## Annualized Sharpe (Rf=0%) 0.8758 0.3347
## Buy & Hold
## Annualized Return 0.1584
## Annualized Std Dev 0.2500
## Annualized Sharpe (Rf=0%) 0.6338charts.PerformanceSummary(smicomp)
# 5.10. Williams %R(14) Strategy Performance Comparison
# Bands Crossover Strategy Returns/Equity Curve
wprstrat <- ret*wprsig
wprstratc <- ifelse((wprtr==1|wprtr==-1)&wprsig!=Lag(wprsig),(ret-0.01)*wprsig,ret*wprsig)
# Bands Crossover Strategy Performance Comparison
wprcomp <- cbind(wprstrat,wprstratc,bhstrat)
colnames(wprcomp) <- c("%R(14)","%R(14) TC","Buy & Hold")
table.AnnualizedReturns(wprcomp)## %R(14) %R(14) TC Buy & Hold
## Annualized Return 0.0208 -0.0381 0.1584
## Annualized Std Dev 0.1724 0.1744 0.2500
## Annualized Sharpe (Rf=0%) 0.1204 -0.2184 0.6338charts.PerformanceSummary(wprcomp)
# 5.11. Commodity Channel Index CCI(20,0.015) and Simple Moving Average SMA(5) Strategy Performance Comparison
# Price Crossover and Bands Crossover Confirmation Strategy Returns/Equity Curve
ccismastrat <- ret*ccismasig
ccismastratc <- ifelse((ccismatr==1|ccismatr==-1)&ccismasig!=Lag(ccismasig),(ret-0.01)*ccismasig,ret*ccismasig)
# Price Crossover and Bands Crossover Confirmation Strategy Performance Comparison
ccismacomp <- cbind(ccismastrat,ccismastratc,bhstrat)
colnames(ccismacomp) <- c("SMA(5) and CCI(20,0.015)","SMA(5) and CCI(20,0.015) TC","Buy & Hold")
table.AnnualizedReturns(ccismacomp)## SMA(5) and CCI(20,0.015)
## Annualized Return 0.0380
## Annualized Std Dev 0.1796
## Annualized Sharpe (Rf=0%) 0.2115
## SMA(5) and CCI(20,0.015) TC Buy & Hold
## Annualized Return 0.0258 0.1584
## Annualized Std Dev 0.1795 0.2500
## Annualized Sharpe (Rf=0%) 0.1439 0.6338charts.PerformanceSummary(ccismacomp)
# 5.12. Rate Of Change ROC(21) and Simple Moving Average SMA(5) Strategy Performance Comparison
# Price Crossover and Bands Crossover Confirmation Strategy Returns/Equity Curve
rocsmastrat <- ret*rocsmasig
rocsmastratc <- ifelse((rocsmatr==1|rocsmatr==-1)&rocsmasig!=Lag(rocsmasig),(ret-0.01)*rocsmasig,ret*rocsmasig)
# Price Crossover and Bands Crossover Confirmation Strategy Performance Comparison
rocsmacomp <- cbind(rocsmastrat,rocsmastratc,bhstrat)
colnames(rocsmacomp) <- c("SMA(5) and ROC(21)","SMA(5) and ROC(21) TC","Buy & Hold")
table.AnnualizedReturns(rocsmacomp)## SMA(5) and ROC(21) SMA(5) and ROC(21) TC
## Annualized Return -0.0140 -0.0173
## Annualized Std Dev 0.1645 0.1645
## Annualized Sharpe (Rf=0%) -0.0850 -0.1052
## Buy & Hold
## Annualized Return 0.1584
## Annualized Std Dev 0.2500
## Annualized Sharpe (Rf=0%) 0.6338charts.PerformanceSummary(rocsmacomp)
# 5.13. Relative Strength Index RSI(14) and Simple Moving Average SMA(5) Strategy Performance Comparison
# Price Crossover and Bands Crossover Confirmation Strategy Returns/Equity Curve
rsismastrat <- ret*rsismasig
rsismastratc <- ifelse((rsismatr==1|rsismatr==-1)&rsismasig!=Lag(rsismasig),(ret-0.01)*rsismasig,ret*rsismasig)
# Price Crossover and Bands Crossover Confirmation Strategy Performance Comparison
rsismacomp <- cbind(rsismastrat,rsismastratc,bhstrat)
colnames(rsismacomp) <- c("SMA(5) and RSI(14)","SMA(5) and RSI(14) TC","Buy & Hold")
table.AnnualizedReturns(rsismacomp)## SMA(5) and RSI(14) SMA(5) and RSI(14) TC
## Annualized Return 0.0973 0.0954
## Annualized Std Dev 0.0799 0.0803
## Annualized Sharpe (Rf=0%) 1.2168 1.1884
## Buy & Hold
## Annualized Return 0.1584
## Annualized Std Dev 0.2500
## Annualized Sharpe (Rf=0%) 0.6338charts.PerformanceSummary(rsismacomp)
# 5.14. Stochastic Momentum Index SMI(13,2,25,9) and Simple Moving Average SMA(5) Strategy Performance Comparison
# Price Crossover and Bands Crossover Confirmation Strategy Returns/Equity Curve
smismastrat <- ret*smismasig
smismastratc <- ifelse((smismatr==1|smismatr==-1)&smismasig!=Lag(smismasig),(ret-0.01)*smismasig,ret*smismasig)
# Price Crossover and Bands Crossover Confirmation Strategy Performance Comparison
smismacomp <- cbind(smismastrat,smismastratc,bhstrat)
colnames(smismacomp) <- c("SMA(5) and SMI(13,2,25)","SMA(5) and SMI(13,2,25) TC","Buy & Hold")
table.AnnualizedReturns(smismacomp)## SMA(5) and SMI(13,2,25)
## Annualized Return 0.0036
## Annualized Std Dev 0.1548
## Annualized Sharpe (Rf=0%) 0.0231
## SMA(5) and SMI(13,2,25) TC Buy & Hold
## Annualized Return -0.0034 0.1584
## Annualized Std Dev 0.1560 0.2500
## Annualized Sharpe (Rf=0%) -0.0215 0.6338charts.PerformanceSummary(smismacomp)
# 5.15. Williams %R(14) and Simple Moving Average SMA(5) Strategy Performance Comparison
# Price Crossover and Bands Crossover Confirmation Strategy Returns/Equity Curve
wprsmastrat <- ret*wprsmasig
wprsmastratc <- ifelse((wprsmatr==1|wprsmatr==-1)&wprsmasig!=Lag(wprsmasig),(ret-0.01)*wprsmasig,ret*wprsmasig)
# Price Crossover and Bands Crossover Confirmation Strategy Performance Comparison
wprsmacomp <- cbind(wprsmastrat,wprsmastratc,bhstrat)
colnames(wprsmacomp) <- c("SMA(5) and %R(14)","SMA(5) and %R(14) TC","Buy & Hold")
table.AnnualizedReturns(wprsmacomp)## SMA(5) and %R(14) SMA(5) and %R(14) TC
## Annualized Return 0.0687 0.0651
## Annualized Std Dev 0.0858 0.0858
## Annualized Sharpe (Rf=0%) 0.8004 0.7589
## Buy & Hold
## Annualized Return 0.1584
## Annualized Std Dev 0.2500
## Annualized Sharpe (Rf=0%) 0.6338charts.PerformanceSummary(wprsmacomp)
References:
You can find the references to the code and the pictures in the following document in the the below links:
- https://www.investopedia.com/terms/c/candlestick.asp
- https://www.udemy.com/stock-technical-analysis-with-r/learn/v4/overview
- https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/list-of-technical-indicators/
- http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators
- https://www.investopedia.com/terms/t/technicalindicator.asp